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Found 3 papers, 2 papers with code
Practical and scalable simulations of non-Markovian stochastic processes
While analytic solutions often cannot be derived, existing simulation frameworks can generate stochastic trajectories compatible with the dynamical laws underlying the random phenomena.
High-Dimensional Inference in Bayesian Networks
Inference of the marginal probability distribution is defined as the calculation of the probability of a subset of the variables and is relevant for handling missing data and hidden variables.
Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG
The package includes tools to search for a maximum a posteriori (MAP) graph and to sample graphs from the posterior distribution given the data.
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